simulation.cc

// -*- coding: utf-8 -*-
// Copyright (C) 2012, 2013, 2014, 2015 Laboratoire de Recherche et
// Développement de l'Epita (LRDE).
//
// This file is part of Spot, a model checking library.
//
// Spot is free software; you can redistribute it and/or modify it
// under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 3 of the License, or
// (at your option) any later version.
//
// Spot is distributed in the hope that it will be useful, but WITHOUT
// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
// or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public
// License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program.  If not, see <http://www.gnu.org/licenses/>.

#include <queue>
#include <map>
#include <utility>
#include <cmath>
#include <limits>
#include "simulation.hh"
#include "misc/minato.hh"
#include "tgba/bddprint.hh"
#include "tgbaalgos/reachiter.hh"
#include "tgbaalgos/sccfilter.hh"
#include "tgbaalgos/sccinfo.hh"
#include "tgbaalgos/dupexp.hh"
#include "misc/bddlt.hh"

// The way we developed this algorithm is the following: We take an
// automaton, and reverse all these acceptance conditions.  We reverse
// them to go make the meaning of the signature easier. We are using
// bdd, and we want to let it make all the simplification. Because of
// the format of the acceptance condition, it doesn't allow easy
// simplification. Instead of encoding them as: "a!b!c + !ab!c", we
// use them as: "ab". We complement them because we want a
// simplification if the condition of the transition A implies the
// transition of B, and if the acceptance condition of A is included
// in the acceptance condition of B. To let the bdd makes the job, we
// revert them.

// Then, to check if a transition i-dominates another, we'll use the bdd:
// "sig(transA) = cond(trans) & acc(trans) & implied(class(trans->state))"
// Idem for sig(transB). The 'implied'
// (represented by a hash table 'relation_' in the implementation) is
// a conjunction of all the class dominated by the class of the
// destination. This is how the relation is included in the
// signature. It makes the simplifications alone, and the work is
// done.  The algorithm is cut into several step:
//
// 1. Run through the tgba and switch the acceptance condition to their
//    negation, and initializing relation_ by the 'init_ -> init_' where
//    init_ is the bdd which represents the class. This function is the
//    constructor of Simulation.
// 2. Enter in the loop (run).
//    - Rename the class.
//    - run through the automaton and computing the signature of each
//      state. This function is `update_sig'.
//    - Enter in a double loop to adapt the partial order, and set
//      'relation_' accordingly. This function is `update_po'.
// 3. Rename the class (to actualize the name in the previous_class and
//    in relation_).
// 4. Building an automaton with the result, with the condition:
// "a transition in the original automaton appears in the simulated one
// iff this transition is included in the set of i-maximal neighbour."
// This function is `build_output'.
// The automaton simulated is recomplemented to come back to its initial
// state when the object Simulation is destroyed.
//
// Obviously these functions are possibly cut into several little ones.
// This is just the general development idea.


namespace spot
{
  namespace
  {
    // Some useful typedef:

    // Used to get the signature of the state.
    typedef std::vector<bdd> vector_state_bdd;

    typedef std::vector<const state*> vector_state_state;


    // Get the list of state for each class.
    typedef std::map<bdd, std::list<unsigned>,
                     bdd_less_than> map_bdd_lstate;

    typedef std::map<bdd, unsigned, bdd_less_than> map_bdd_state;


    // This class helps to compare two automata in term of
    // size.
    struct automaton_size
    {
      automaton_size()
        : transitions(0),
          states(0)
      {
      }

      automaton_size(const tgba_digraph_ptr& a)
        : transitions(a->num_transitions()),
          states(a->num_states())
      {
      }

      void set_size(const tgba_digraph_ptr& a)
      {
	states = a->num_states();
	transitions = a->num_transitions();
      }

      inline bool operator!=(const automaton_size& r)
      {
        return transitions != r.transitions || states != r.states;
      }

      inline bool operator<(const automaton_size& r)
      {
        if (states < r.states)
          return true;
        if (states > r.states)
          return false;

        if (transitions < r.transitions)
          return true;
        if (transitions > r.transitions)
          return false;

        return false;
      }

      inline bool operator>(const automaton_size& r)
      {
        if (states < r.states)
          return false;
        if (states > r.states)
          return true;

        if (transitions < r.transitions)
          return false;
        if (transitions > r.transitions)
          return true;

        return false;
      }

      int transitions;
      int states;
    };

    // The direct_simulation. If Cosimulation is true, we are doing a
    // cosimulation.
    template <bool Cosimulation, bool Sba>
    class direct_simulation
    {
    protected:
      // Shortcut used in update_po and go_to_next_it.
      typedef std::map<bdd, bdd, bdd_less_than> map_bdd_bdd;
      int acc_vars;
    public:

      bdd mark_to_bdd(acc_cond::mark_t m)
      {
	// FIXME: Use a cache.
	bdd res = bddtrue;
	for (auto n: a_->acc().sets(m))
	  res &= bdd_ithvar(acc_vars + n);
	return res;
      }

      acc_cond::mark_t bdd_to_mark(const tgba_digraph_ptr& aut, bdd b)
      {
	// FIXME: Use a cache.
	std::vector<unsigned> res;
	while (b != bddtrue)
	  {
	    res.push_back(bdd_var(b) - acc_vars);
	    b = bdd_high(b);
	  }
	return aut->acc().marks(res.begin(), res.end());
      }

      direct_simulation(const const_tgba_ptr& t)
        : a_(0),
          po_size_(0),
          all_class_var_(bddtrue),
          original_(t)
      {
        // We need to do a dupexp for being able to run scc_info later.
        // new_original_ is the map that contains the relation between
        // the names (addresses) of the states in the automaton
        // returned by dupexp, and in automaton given in argument to
        // the constructor.
        a_ = tgba_dupexp_dfs(t, { true, true, true, true }, new_original_);
        scc_info_.reset(new scc_info(a_));
        old_a_ = a_;

	// Replace all the acceptance conditions by their complements.
	// (In the case of Cosimulation, we also flip the transitions.)
	{
	  if (Cosimulation)
	    {
	      a_ = make_tgba_digraph(a_->get_dict());
	      a_->copy_ap_of(old_a_);
	      a_->copy_acceptance_conditions_of(old_a_);
	    }
	  unsigned ns = old_a_->num_states();
	  if (Cosimulation)
	    a_->new_states(ns);
	  for (unsigned s = 0; s < ns; ++s)
	    {
	      for (auto& t: old_a_->out(s))
		{
		  acc_cond::mark_t acc;
		  if (Sba && Cosimulation)
		    {
		      // If the acceptance is interpreted as
		      // state-based, to apply the reverse simulation
		      // on a SBA, we should pull the acceptance of
		      // the destination state on its incoming arcs
		      // (which now become outgoing arcs after
		      // transposition).
		      acc = 0U;
		      for (auto& td: old_a_->out(t.dst))
			{
			  acc = old_a_->acc().comp(td.acc);
			  break;
			}
		    }
		  else
		    {
		      acc = old_a_->acc().comp(t.acc);
		    }
		  if (Cosimulation)
		    a_->new_transition(t.dst, s, t.cond, acc);
		  else
		    t.acc = acc;
		}
	      if (Cosimulation)
		a_->set_init_state(old_a_->get_init_state_number());
	    }
	  size_a_ = ns;
	}

	// Now, we have to get the bdd which will represent the
	// class. We register one bdd by state, because in the worst
	// case, |Class| == |State|.
	unsigned set_num = a_->get_dict()
	  ->register_anonymous_variables(size_a_ + 1, this);

	unsigned n_acc = a_->acc().num_sets();
	acc_vars = a_->get_dict()
	  ->register_anonymous_variables(n_acc, this);

	all_proms_ = bddtrue;
	for (unsigned v = acc_vars; v < acc_vars + n_acc; ++v)
	  all_proms_ &= bdd_ithvar(v);

        bdd_initial = bdd_ithvar(set_num++);
	bdd init = bdd_ithvar(set_num++);

	used_var_.push_back(init);

	// Initialize all classes to init.
	previous_class_.resize(size_a_);
	for (unsigned s = 0; s < size_a_; ++s)
	  previous_class_[s] = init;

	// Put all the anonymous variable in a queue, and record all
	// of these in a variable all_class_var_ which will be used
	// to understand the destination part in the signature when
	// building the resulting automaton.
	all_class_var_ = init;
	for (unsigned i = set_num; i < set_num + size_a_ - 1; ++i)
          {
            free_var_.push(i);
            all_class_var_ &= bdd_ithvar(i);
          }

	relation_[init] = init;
      }


      // Reverse all the acceptance condition at the destruction of
      // this object, because it occurs after the return of the
      // function simulation.
      virtual ~direct_simulation()
      {
	a_->get_dict()->unregister_all_my_variables(this);
      }

      // Update the name of the classes.
      void update_previous_class()
      {
	std::list<bdd>::iterator it_bdd = used_var_.begin();

	// We run through the map bdd/list<state>, and we update
	// the previous_class_ with the new data.
	for (auto& p: bdd_lstate_)
          {
	    // If the signature of a state is bddfalse (no
	    // transitions) the class of this state is bddfalse
	    // instead of an anonymous variable. It allows
	    // simplifications in the signature by removing a
	    // transition which has as a destination a state with
	    // no outgoing transition.
	    if (p.first == bddfalse)
	      for (auto s: p.second)
		previous_class_[s] = bddfalse;
	    else
	      for (auto s: p.second)
		previous_class_[s] = *it_bdd;
	    ++it_bdd;
          }
      }

      void main_loop()
      {
	unsigned int nb_partition_before = 0;
	unsigned int nb_po_before = po_size_ - 1;
	while (nb_partition_before != bdd_lstate_.size()
	       || nb_po_before != po_size_)
          {
            update_previous_class();
            nb_partition_before = bdd_lstate_.size();
            bdd_lstate_.clear();
            nb_po_before = po_size_;
            po_size_ = 0;
            update_sig();
            go_to_next_it();
          }

	update_previous_class();
      }

      // The core loop of the algorithm.
      tgba_digraph_ptr run()
      {
        main_loop();
	return build_result();
      }

      // Take a state and compute its signature.
      bdd compute_sig(unsigned src)
      {
        bdd res = bddfalse;

        for (auto& t: a_->out(src))
          {
            bdd acc = mark_to_bdd(t.acc);

	    // to_add is a conjunction of the acceptance condition,
	    // the label of the transition and the class of the
	    // destination and all the class it implies.
	    bdd to_add = acc & t.cond & relation_[previous_class_[t.dst]];

	    res |= to_add;
	  }

        // When we Cosimulate, we add a special flag to differentiate
        // the initial state from the other.
        if (Cosimulation && src == a_->get_init_state_number())
          res |= bdd_initial;

        return res;
      }


      void update_sig()
      {
	for (unsigned s = 0; s < size_a_; ++s)
	  bdd_lstate_[compute_sig(s)].push_back(s);
      }


      // This method rename the color set, update the partial order.
      void go_to_next_it()
      {
	int nb_new_color = bdd_lstate_.size() - used_var_.size();


        // If we have created more partitions, we need to use more
        // variables.
	for (int i = 0; i < nb_new_color; ++i)
          {
            assert(!free_var_.empty());
            used_var_.push_back(bdd_ithvar(free_var_.front()));
            free_var_.pop();
          }


        // If we have reduced the number of partition, we 'free' them
        // in the free_var_ list.
	for (int i = 0; i > nb_new_color; --i)
          {
            assert(!used_var_.empty());
            free_var_.push(bdd_var(used_var_.front()));
            used_var_.pop_front();
          }


	assert((bdd_lstate_.size() == used_var_.size())
               || (bdd_lstate_.find(bddfalse) != bdd_lstate_.end()
                   && bdd_lstate_.size() == used_var_.size() + 1));

	// Now we make a temporary hash_table which links the tuple
	// "C^(i-1), N^(i-1)" to the new class coloring.  If we
	// rename the class before updating the partial order, we
	// loose the information, and if we make it after, I can't
	// figure out how to apply this renaming on rel_.
	// It adds a data structure but it solves our problem.
	map_bdd_bdd now_to_next;

	std::list<bdd>::iterator it_bdd = used_var_.begin();

	for (auto& p: bdd_lstate_)
          {
	    // If the signature of a state is bddfalse (no
	    // transitions) the class of this state is bddfalse
	    // instead of an anonymous variable. It allows
	    // simplifications in the signature by removing a
	    // transition which has as a destination a state with
	    // no outgoing transition.
	    bdd acc = bddfalse;
	    if (p.first != bddfalse)
	      acc = *it_bdd;
	    now_to_next[p.first] = acc;
	    ++it_bdd;
          }

	update_po(now_to_next, relation_);
      }

      // This function computes the new po with previous_class_ and
      // the argument. `now_to_next' contains the relation between the
      // signature and the future name of the class.  We need a
      // template parameter because we use this function with a
      // map_bdd_bdd, but later, we need a list_bdd_bdd. So to
      // factorize some code we use a template.
      template <typename container_bdd_bdd>
      void update_po(const container_bdd_bdd& now_to_next,
                     map_bdd_bdd& relation)
      {
	// This loop follows the pattern given by the paper.
	// foreach class do
	// |  foreach class do
	// |  | update po if needed
	// |  od
	// od

	for (typename container_bdd_bdd::const_iterator it1
               = now_to_next.begin();
	     it1 != now_to_next.end();
	     ++it1)
          {
            bdd accu = it1->second;
            for (typename container_bdd_bdd::const_iterator it2
                   = now_to_next.begin();
                 it2 != now_to_next.end();
                 ++it2)
              {
                // Skip the case managed by the initialization of accu.
                if (it1 == it2)
                  continue;

                if (bdd_implies(it1->first, it2->first))
                  {
                    accu &= it2->second;
                    ++po_size_;
                  }
              }
            relation[it1->second] = accu;
          }
      }

      // Build the minimal resulting automaton.
      tgba_digraph_ptr build_result()
      {
	tgba_digraph_ptr res = make_tgba_digraph(a_->get_dict());
	res->copy_ap_of(a_);
	res->copy_acceptance_conditions_of(a_);
	if (Sba)
	  res->prop_state_based_acc();

	// Non atomic propositions variables (= acc and class)
	bdd nonapvars = all_proms_ & bdd_support(all_class_var_);

	auto* gb = res->create_namer<int>();

	// Create one state per partition.
	for (auto& p: bdd_lstate_)
          {
            bdd cl = previous_class_[p.second.front()];
	    // A state may be referred to either by
	    // its class, or by all the implied classes.
	    auto s = gb->new_state(cl.id());
	    gb->alias_state(s, relation_[cl].id());
          }

	// Acceptance of states.  Only used if Sba && Cosimulation.
	std::vector<acc_cond::mark_t> accst;
	if (Sba && Cosimulation)
	  accst.resize(res->num_states(), 0U);

        stat.states = bdd_lstate_.size();
        stat.transitions = 0;

        unsigned nb_satoneset = 0;
        unsigned nb_minato = 0;

	// For each class, we will create
	// all the transitions between the states.
	for (auto& p: bdd_lstate_)
          {
	    // All states in p.second have the same class, so just
	    // pick the class of the first one first one.
	    bdd src = previous_class_[p.second.front()];

            // Get the signature to derive successors.
            bdd sig = compute_sig(p.second.front());

            if (Cosimulation)
              sig = bdd_compose(sig, bddfalse, bdd_var(bdd_initial));

            // Get all the variable in the signature.
            bdd sup_sig = bdd_support(sig);

            // Get the variable in the signature which represents the
            // conditions.
            bdd sup_all_atomic_prop = bdd_exist(sup_sig, nonapvars);

            // Get the part of the signature composed only with the atomic
            // proposition.
            bdd all_atomic_prop = bdd_exist(sig, nonapvars);

	    // First loop over all possible valuations atomic properties.
            while (all_atomic_prop != bddfalse)
	      {
		bdd one = bdd_satoneset(all_atomic_prop,
					sup_all_atomic_prop,
					bddtrue);
		all_atomic_prop -= one;

		// For each possible valuation, iterate over all possible
		// destination classes.   We use minato_isop here, because
		// if the same valuation of atomic properties can go
		// to two different classes C1 and C2, iterating on
		// C1 + C2 with the above bdd_satoneset loop will see
		// C1 then (!C1)C2, instead of C1 then C2.
		// With minatop_isop, we ensure that the no negative
		// class variable will be seen (likewise for promises).
		minato_isop isop(sig & one);

                ++nb_satoneset;

		bdd cond_acc_dest;
		while ((cond_acc_dest = isop.next()) != bddfalse)
		  {
                    ++stat.transitions;

                    ++nb_minato;

		    // Take the transition, and keep only the variable which
		    // are used to represent the class.
		    bdd dst = bdd_existcomp(cond_acc_dest,
					     all_class_var_);

		    // Keep only ones who are acceptance condition.
		    auto acc = bdd_to_mark(res, bdd_existcomp(cond_acc_dest,
							      all_proms_));

		    // Keep the other!
		    bdd cond = bdd_existcomp(cond_acc_dest,
					     sup_all_atomic_prop);

		    // Because we have complemented all the acceptance
		    // conditions on the input automaton, we must
		    // revert them to create a new transition.
		    acc = res->acc().comp(acc);

		    if (Cosimulation)
		      {
			if (Sba)
			  {
			    // acc should be attached to src, or rather,
			    // in our transition-based representation)
			    // to all transitions leaving src.  As we
			    // can't do this here, store this in a table
			    // so we can fix it later.
			    accst[gb->get_state(src.id())] = acc;
			    acc = 0U;
			  }
			gb->new_transition(dst.id(), src.id(), cond, acc);
		      }
		    else
		      {
			gb->new_transition(src.id(), dst.id(), cond, acc);
		      }
		  }
	      }
          }

	res->set_init_state(gb->get_state(previous_class_
					  [a_->get_init_state_number()].id()));

	res->merge_transitions(); // FIXME: is this really needed?

	// Mark all accepting state in a second pass, when
	// dealing with SBA in cosimulation.
	if (Sba && Cosimulation)
	  {
	    unsigned ns = res->num_states();
	    for (unsigned s = 0; s < ns; ++s)
	      {
		acc_cond::mark_t acc = accst[s];
		if (acc == 0U)
		  continue;
		for (auto& t: res->out(s))
		  t.acc = acc;
	      }
	  }

	res->purge_unreachable_states();

	delete gb;
	res->prop_copy(original_,
		       { false, // state-based acc forced below
			 false, // single acc set by set_acceptance_conditions
		         true,  // weakness preserved,
			 false, // determinism checked and set below
		       });
        if (nb_minato == nb_satoneset)
	  res->prop_deterministic();
	if (Sba)
	  res->prop_state_based_acc();

	return res;
      }


      // Debug:
      // In a first time, print the signature, and the print a list
      // of each state in this partition.
      // In a second time, print foreach state, who is where,
      // where is the new class name.
      void print_partition()
      {
	for (auto& p: bdd_lstate_)
          {
            std::cerr << "partition: "
                      << bdd_format_isop(a_->get_dict(), p.first)
                      << std::endl;
            for (auto s: p.second)
	      std::cerr << "  - "
			<< a_->format_state(a_->state_from_number(s))
			<< '\n';
          }

	std::cerr << "\nPrevious iteration\n" << std::endl;

	unsigned ps = previous_class_.size();
	for (unsigned p = 0; p < ps; ++p)
          {
            std::cerr << a_->format_state(a_->state_from_number(p))
                      << " was in "
                      << bdd_format_set(a_->get_dict(), previous_class_[p])
                      << '\n';
          }
      }

    protected:
      // The automaton which is simulated.
      tgba_digraph_ptr a_;
      tgba_digraph_ptr old_a_;

      // Relation is aimed to represent the same thing than
      // rel_. The difference is in the way it does.
      // If A => A /\ A => B, rel will be (!A U B), but relation_
      // will have A /\ B at the key A. This trick is due to a problem
      // with the computation of the resulting automaton with the signature.
      // rel_ will pollute the meaning of the signature.
      map_bdd_bdd relation_;

      // Represent the class of each state at the previous iteration.
      vector_state_bdd previous_class_;

      // The list of state for each class at the current_iteration.
      // Computed in `update_sig'.
      map_bdd_lstate bdd_lstate_;

      // The queue of free bdd. They will be used as the identifier
      // for the class.
      std::queue<int> free_var_;

      // The list of used bdd. They are in used as identifier for class.
      std::list<bdd> used_var_;

      // Size of the automaton.
      unsigned int size_a_;

      // Used to know when there is no evolution in the po. Updated
      // in the `update_po' method.
      unsigned int po_size_;

      // All the class variable:
      bdd all_class_var_;

      // The flag to say if the outgoing state is initial or not
      bdd bdd_initial;

      bdd all_proms_;

      automaton_size stat;

      std::unique_ptr<scc_info> scc_info_;
      std::vector<const state*> new_original_;

      const_tgba_ptr original_;
    };

  } // End namespace anonymous.


  tgba_digraph_ptr
  simulation(const const_tgba_ptr& t)
  {
    direct_simulation<false, false> simul(t);
    return simul.run();
  }

  tgba_digraph_ptr
  simulation_sba(const const_tgba_ptr& t)
  {
    direct_simulation<false, true> simul(t);
    return simul.run();
  }

  tgba_digraph_ptr
  cosimulation(const const_tgba_ptr& t)
  {
    direct_simulation<true, false> simul(t);
    return simul.run();
  }

  tgba_digraph_ptr
  cosimulation_sba(const const_tgba_ptr& t)
  {
    direct_simulation<true, true> simul(t);
    return simul.run();
  }


  template<bool Sba>
  tgba_digraph_ptr
  iterated_simulations_(const const_tgba_ptr& t)
  {
    tgba_digraph_ptr res = 0;
    automaton_size prev;
    automaton_size next;

    do
      {
        prev = next;
        direct_simulation<false, Sba> simul(res ? res : t);
        res = simul.run();
        if (res->is_deterministic())
	  break;

        direct_simulation<true, Sba> cosimul(res);
	res = cosimul.run();

	if (Sba)
	  res = scc_filter_states(res);
	else
	  res = scc_filter(res, false);

        next.set_size(res);
      }
    while (prev != next);
    return res;
  }

  tgba_digraph_ptr
  iterated_simulations(const const_tgba_ptr& t)
  {
    return iterated_simulations_<false>(t);
  }

  tgba_digraph_ptr
  iterated_simulations_sba(const const_tgba_ptr& t)
  {
    return iterated_simulations_<true>(t);
  }

} // End namespace spot.